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mtzig/
v3b_mistral_lora

Name: v3b_mistral_lora
Availability: InStock

peft

safetensors

generated_from_trainer

peiyi9979/math-shepherd-mistral-7b-prm

adapter

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v3b_mistral_lora

This model is a fine-tuned version of peiyi9979/math-shepherd-mistral-7b-prm on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2975
Accuracy: 0.8647
Precision: 0.8701
Recall: 0.6087
F1: 0.7163

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 765837
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	0	0	0.6026	0.7339	0.6	0.1542	0.2453
0.5256	0.0095	20	0.6015	0.7350	0.6061	0.1581	0.2508
0.6118	0.0189	40	0.5988	0.7361	0.6087	0.1660	0.2609
0.5575	0.0284	60	0.5849	0.7450	0.6456	0.2016	0.3072
0.6385	0.0378	80	0.5648	0.7461	0.5938	0.3004	0.3990
0.4791	0.0473	100	0.5396	0.7661	0.6694	0.3281	0.4403
0.3593	0.0567	120	0.5030	0.7794	0.7109	0.3597	0.4777
0.4435	0.0662	140	0.4716	0.7794	0.6467	0.4704	0.5446
0.3899	0.0757	160	0.4403	0.7938	0.7519	0.3953	0.5181
0.3429	0.0851	180	0.4055	0.8160	0.7771	0.4822	0.5951
0.3529	0.0946	200	0.3847	0.8182	0.7405	0.5415	0.6256
0.36	0.1040	220	0.3824	0.8182	0.7697	0.5020	0.6077
0.2875	0.1135	240	0.3578	0.8226	0.7385	0.5692	0.6429
0.3237	0.1229	260	0.3457	0.8426	0.7342	0.6877	0.7102
0.2309	0.1324	280	0.3626	0.8204	0.8527	0.4348	0.5759
0.2843	0.1418	300	0.3511	0.8326	0.8493	0.4901	0.6216
0.2694	0.1513	320	0.3487	0.8337	0.8411	0.5020	0.6287
0.3854	0.1608	340	0.3573	0.8193	0.8358	0.4427	0.5788
0.3062	0.1702	360	0.3262	0.8470	0.7778	0.6364	0.7
0.2861	0.1797	380	0.3308	0.8459	0.8202	0.5771	0.6775
0.2808	0.1891	400	0.3584	0.8337	0.8931	0.4625	0.6094
0.2716	0.1986	420	0.3312	0.8525	0.8614	0.5652	0.6826
0.3696	0.2080	440	0.3196	0.8548	0.8020	0.6403	0.7121
0.1911	0.2175	460	0.3436	0.8426	0.8725	0.5138	0.6468
0.2548	0.2270	480	0.3311	0.8525	0.8704	0.5573	0.6795
0.2501	0.2364	500	0.3237	0.8481	0.8671	0.5415	0.6667
0.2936	0.2459	520	0.3496	0.8359	0.8832	0.4783	0.6205
0.2012	0.2553	540	0.3362	0.8404	0.8516	0.5217	0.6471
0.3295	0.2648	560	0.3415	0.8492	0.8462	0.5652	0.6777
0.2859	0.2742	580	0.3370	0.8437	0.8733	0.5178	0.6501
0.2655	0.2837	600	0.3248	0.8492	0.8343	0.5771	0.6822
0.2646	0.2931	620	0.3290	0.8481	0.8625	0.5455	0.6683
0.2706	0.3026	640	0.3193	0.8481	0.8222	0.5850	0.6836
0.2074	0.3121	660	0.3506	0.8470	0.8912	0.5178	0.655
0.2825	0.3215	680	0.3523	0.8282	0.8828	0.4466	0.5932
0.2718	0.3310	700	0.3708	0.8271	0.9008	0.4308	0.5829
0.2172	0.3404	720	0.3735	0.8237	0.9123	0.4111	0.5668
0.1876	0.3499	740	0.3519	0.8392	0.9154	0.4704	0.6214
0.2788	0.3593	760	0.3574	0.8348	0.8611	0.4901	0.6247
0.305	0.3688	780	0.3154	0.8581	0.8492	0.6008	0.7037
0.2726	0.3783	800	0.3149	0.8459	0.875	0.5257	0.6568
0.2819	0.3877	820	0.3015	0.8581	0.7880	0.6759	0.7277
0.2596	0.3972	840	0.3099	0.8548	0.7629	0.6996	0.7299
0.185	0.4066	860	0.3079	0.8614	0.8299	0.6364	0.7204
0.189	0.4161	880	0.3248	0.8503	0.8882	0.5336	0.6667
0.299	0.4255	900	0.3174	0.8525	0.8614	0.5652	0.6826
0.199	0.4350	920	0.3387	0.8392	0.9030	0.4783	0.6253
0.2886	0.4444	940	0.3313	0.8381	0.8794	0.4901	0.6294
0.2641	0.4539	960	0.3095	0.8636	0.8611	0.6126	0.7159
0.2316	0.4634	980	0.3030	0.8603	0.8256	0.6364	0.7188
0.2116	0.4728	1000	0.3230	0.8581	0.8571	0.5929	0.7009
0.2134	0.4823	1020	0.3040	0.8625	0.8057	0.6719	0.7328
0.2139	0.4917	1040	0.3280	0.8448	0.9007	0.5020	0.6447
0.1949	0.5012	1060	0.3116	0.8625	0.8728	0.5968	0.7089
0.2255	0.5106	1080	0.3195	0.8592	0.8663	0.5889	0.7012
0.2452	0.5201	1100	0.3464	0.8426	0.8936	0.4980	0.6396
0.2038	0.5296	1120	0.3167	0.8570	0.8735	0.5731	0.6921
0.2496	0.5390	1140	0.3181	0.8592	0.8795	0.5771	0.6969
0.2864	0.5485	1160	0.3201	0.8514	0.8790	0.5455	0.6732
0.2342	0.5579	1180	0.3140	0.8647	0.8429	0.6364	0.7252
0.1366	0.5674	1200	0.3010	0.8681	0.8384	0.6561	0.7361
0.2301	0.5768	1220	0.3011	0.8625	0.8564	0.6126	0.7143
0.2873	0.5863	1240	0.3049	0.8625	0.8564	0.6126	0.7143
0.2467	0.5957	1260	0.3107	0.8625	0.8686	0.6008	0.7103
0.3175	0.6052	1280	0.3120	0.8581	0.8788	0.5731	0.6938
0.1988	0.6147	1300	0.3020	0.8636	0.8652	0.6087	0.7146
0.2081	0.6241	1320	0.3175	0.8559	0.8820	0.5613	0.6860
0.1784	0.6336	1340	0.2959	0.8647	0.8227	0.6601	0.7325
0.2712	0.6430	1360	0.3133	0.8592	0.85	0.6047	0.7067
0.2463	0.6525	1380	0.3180	0.8548	0.8427	0.5929	0.6961
0.3991	0.6619	1400	0.3167	0.8625	0.8817	0.5889	0.7062
0.154	0.6714	1420	0.3027	0.8636	0.8652	0.6087	0.7146
0.1944	0.6809	1440	0.3172	0.8625	0.8772	0.5929	0.7075
0.2434	0.6903	1460	0.3035	0.8692	0.8325	0.6680	0.7412
0.2346	0.6998	1480	0.3163	0.8625	0.8728	0.5968	0.7089
0.2532	0.7092	1500	0.2938	0.8659	0.83	0.6561	0.7329
0.1815	0.7187	1520	0.3156	0.8570	0.8924	0.5573	0.6861
0.1989	0.7281	1540	0.3187	0.8614	0.8855	0.5810	0.7017
0.1749	0.7376	1560	0.3169	0.8647	0.8786	0.6008	0.7136
0.2141	0.7470	1580	0.3046	0.8670	0.8556	0.6324	0.7273
0.2638	0.7565	1600	0.2976	0.8670	0.8446	0.6443	0.7309
0.2215	0.7660	1620	0.2927	0.8647	0.8359	0.6443	0.7277
0.2587	0.7754	1640	0.3179	0.8647	0.9018	0.5810	0.7067
0.2216	0.7849	1660	0.3046	0.8714	0.8914	0.6166	0.7290
0.2357	0.7943	1680	0.2967	0.8670	0.8556	0.6324	0.7273
0.1972	0.8038	1700	0.3002	0.8659	0.8626	0.6206	0.7218
0.2602	0.8132	1720	0.3001	0.8670	0.8757	0.6126	0.7209
0.1873	0.8227	1740	0.3046	0.8647	0.8786	0.6008	0.7136
0.1663	0.8322	1760	0.2978	0.8659	0.875	0.6087	0.7179
0.363	0.8416	1780	0.2977	0.8647	0.8743	0.6047	0.7150
0.1727	0.8511	1800	0.2989	0.8659	0.875	0.6087	0.7179
0.1995	0.8605	1820	0.3006	0.8625	0.8728	0.5968	0.7089
0.154	0.8700	1840	0.2966	0.8681	0.8681	0.6245	0.7264
0.1821	0.8794	1860	0.2968	0.8670	0.8674	0.6206	0.7235
0.2354	0.8889	1880	0.2952	0.8670	0.8595	0.6285	0.7260
0.3563	0.8983	1900	0.2933	0.8670	0.8556	0.6324	0.7273
0.2716	0.9078	1920	0.2968	0.8647	0.8619	0.6166	0.7189
0.1428	0.9173	1940	0.2970	0.8636	0.8652	0.6087	0.7146
0.2108	0.9267	1960	0.2979	0.8659	0.8667	0.6166	0.7206
0.1501	0.9362	1980	0.2986	0.8670	0.8715	0.6166	0.7222
0.2162	0.9456	2000	0.2984	0.8625	0.8644	0.6047	0.7116
0.3241	0.9551	2020	0.2993	0.8647	0.8701	0.6087	0.7163
0.2289	0.9645	2040	0.2976	0.8659	0.8708	0.6126	0.7193
0.1593	0.9740	2060	0.2984	0.8636	0.8693	0.6047	0.7133
0.2018	0.9835	2080	0.2984	0.8647	0.8659	0.6126	0.7176
0.2018	0.9929	2100	0.2975	0.8647	0.8701	0.6087	0.7163

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3

mtzig/v3b_mistral_lora

v3b_mistral_lora

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mtzig/
v3b_mistral_lora